\section{Conclusion}
\label{sect:final}
In this paper we propose a multi-level selective deduplication scheme for 
snapshot service in VM cloud. 
Inner-VM deduplication localizes backup data dependency and exposes more parallelism  
while cross-VM deduplication with a small common data set
effectively  covers a large amount of duplicated data.
Our solution accomplishes the majority of
potential global deduplication saving while still meets stringent cloud resources requirement. 
Evaluation using real user's VM data shows
our solution can accomplish 75\% of what complete global
deduplication can do. 
Compare to today's widely-used snapshot technique, our scheme reduces almost
two-third of snapshot storage cost.
Finally, our scheme uses a very small amount of memory on each node, and leaves
room for additional optimization we are further studying.
\section*{Acknowledgments}
{
We would like to thank Weicai Chen and Shikun Tian from Alibaba for their kind support, 
and the anonymous referees for their comments.
Wei Zhang has received internship support from Alibaba  for VM backup system development.
This work was supported in part by NSF IIS-1118106.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and
do not necessarily reflect the views of Alibaba or the National Science Foundation.
}
%Noted that 6\% is still significant, which is about 24GB per each VM and for a 1000 node Aliyun cluster,
%this is about 600 terabytes.
 
%Our experiments show th
%our solution can eliminate the majority of data duplication with a tiny fraction of
%block hash index store in memory. It does not only saves valuable system resouces in
%the VM cloud, but also makes deduplication much faster.
%
%
%Using  50 user VM data out of 1322 data disks as the training data and
%with  1.5\% as CDS threshold, we see the total 1198GB of new data is reduced by
%755.8GB, while perfect deduplication can reduce 1017.4GB. So 74.3\% of duplicate blocks are eliminated
%by pre-trained CDS, which is quite satisfactory.

